Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
type: Item
instance of: scholarly article
title: Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
zbMATH Open document ID: 1419.68084
author: Rohit K. Tripathy
author: I. Bilionis
published in: Journal of Computational Physics
publication date: 2019-07-11T00:00:00Z
full work available at URL: https://arxiv.org/abs/1802.00850
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 35R60
Mathematics Subject Classification ID: 65N99
zbMATH DE Number: 7079509
zbMATH Keywords: deep neural networks
zbMATH Keywords: dimensionality reduction
zbMATH Keywords: stochastic elliptic PDE
zbMATH Keywords: uncertainty quantification
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describes a project that uses: AlexNet
describes a project that uses: ImageNet
describes a project that uses: Delve Datasets
describes a project that uses: PyTorch
describes a project that uses: Adam
describes a project that uses: AdaGrad
describes a project that uses: Theano
describes a project that uses: TensorFlow
describes a project that uses: ADADELTA
describes a project that uses: RMSprop
describes a project that uses: FiPy
MaRDI profile type: Publication
OpenAlex ID: W2786232134
arXiv ID: 1802.00850
DOI: 10.1016/J.JCP.2018.08.036
description: scientific article